Optimal Positions of Relay Stations for Cluster-Based Two-Hop Cellular Network

Multihop cellular networks is an exciting and a fledgling area of wireless communication which offers huge potential in terms of coverage enhancement, data-rates, power reduction, and various other quality of service improvements. However, resource allocation in MCN is an NP-hard problem. Hence, significant research needs to be done in this field in order to efficiently design the radio network. In this paper, optimal position of relay stations in a hierarchical cluster-based two-hop cellular network is investigated. Vector algebra has been used to derive general equation for carrier-to-interference ratio (C/I) of a mobile station. It has been observed that when the transmit power of base station (BS) and the gateway (GTW)/relay station (RS) are same, the RSs should be located close to mid-point of BS and the edge of the cell. However, significantly, when the transmit power of the BS is greater than that of the GTW, then the RSs should be placed closer to the edge of the cell, in order to maximize the minimum C/I at any point in the cell. This in turn results in higher modulation technique at the physical layer, and hence, a higher data-rate to all the users in the system.

State-of-the-art machine learning techniques for melanoma skin cancer detection and classification:a comprehensive review

Skin cancer is among the most common and lethal cancer types,with the number of cases increasing dramatically worldwide.If not diagnosed in the nascent stages,it can lead to metastases,resulting in high mortality rates.Skin cancer can be cured if detected early.Consequently,timely and accurate diagnosis of such cancers is currently a key research objective.Various machine learning technologies have been employed in computer-aided diagnosis of skin cancer detection and malignancy classification.Machine learning is a subfield of artificial intelligence(AI)involving models and algorithms which can learn from data and generate predictions on previously unseen data.The traditional biopsy method is applied to diagnose skin cancer,which is a tedious and expensive procedure.Alternatively,machine learning algorithms for cancer diagnosis can aid in its early detection,lowering the workload of specialists while simultaneously enhancing skin lesion diagnostics.This article presented a critical review of select state-of-the-art machine learning techniques used to detect skin cancer.Several studies had been collected,and an analysis of the performance of k-nearest neighbors,support vector machine,and convolutional neural networks algorithms on benchmark datasets was conducted.The shortcomings and disadvantages of each algorithm were briefly discussed.Challenges in detecting skin cancer were highlighted and the scope for future research was proposed.

Oblique Wave Trapping by Vertical Permeable Membrane Barriers Located near a Wall

The effectiveness of a vertical partial flexible porous membrane wave barrier located near a rigid vertical impermeable seawall for trapping obliquely incident surface gravity waves are analyzed in water of uniform depth under the assumption of linear water wave theory and small amplitude membrane barrier response. From the general formulation of the submerged membrane barrier, results for bottom-standing and surface-piercing barriers are computed and analyzed in special cases. Using the eigenfunction expansion method, the boundary-value problems are converted into series relations and then the required unknowns are obtained using the least squares approximation method. Various physical quantities of interests like reflection coefficient, wave energy dissipation, wave forces acting on the membrane barrier and the seawall are computed and analyzed for different values of the wave and structural parameters. The study will be useful in the design of the membrane wave barrier for the creation of tranquility zone in the lee side of the barrier to protect the seawall.

A Novel Signal Processing Coprocessor for <i>n-</i>Dimensional Geometric Algebra Applications

This paper provides an implementation of a novel signal processing co-processor using a Geometric Algebra technique tailored for fast and complex geometric calculations in multiple dimensions. This is the first hardware implementation of Geometric Algebra to specifically address the issue of scalability to multiple (1 - 8) dimensions. This paper presents a detailed description of the implementation, with a particular focus on the techniques of optimization used to improve performance. Results are presented which demonstrate at least 3x performance improvements compared to previously published work.

Comprehensive evaluation of key management hierarchies for outsourced data

Key management is an essential component of a cryptographic access control system with a large number of resources.It manages the secret keys assigned to the system entities in such a way that only authorized users can access a resource.Read access control allows read access of a resource by the authorized users and disallows others.An important objective of a key management is to reduce the secret key storage with each authorized user.To this end,there exist two prominent types of key management hierarchy with single key storage per user used for read access control in data outsourcing scenario:user-based and resource-based.In this work,we analyze the two types of hierarchy with respect to static hierarchy characteristics and dynamic operations such as adding or revoking user authorization.Our analysis shows that the resource-based hierarchies can be a better candidate which is not given equal emphasis in the literature.A new heuristic for minimizing the key management hierarchy is introduced that makes it practical in use even for a large number of users and resources.The performance evaluation of dynamic operations such as adding or revoking a user’s read subscription is shown experimentally to support our analytical results.

Comprehensive evaluation of key management hierarchies for outsourced data

Key management is an essential component of a cryptographic access control system with a large number of resources.It manages the secret keys assigned to the system entities in such a way that only authorized users can access a resource.Read access control allows read access of a resource by the authorized users and disallows others.An important objective of a key management is to reduce the secret key storage with each authorized user.To this end,there exist two prominent types of key management hierarchy with single key storage per user used for read access control in data outsourcing scenario:user-based and resource-based.In this work,we analyze the two types of hierarchy with respect to static hierarchy characteristics and dynamic operations such as adding or revoking user authorization.Our analysis shows that the resource-based hierarchies can be a better candidate which is not given equal emphasis in the literature.A new heuristic for minimizing the key management hierarchy is introduced that makes it practical in use even for a large number of users and resources.The performance evaluation of dynamic operations such as adding or revoking a user’s read subscription is shown experimentally to support our analytical results.